# Evaluating AI Versus Examiner Feedback in Ophthalmology Exit Examinations: A Pilot Study

## TL;DR

This pilot study compares feedback from AI and human examiners in ophthalmology exams, finding that AI offers structured guidance while humans provide practical insights.

## Contribution

The study introduces an AI-based platform for simulating ophthalmology exams and delivering structured feedback.

## Key findings

- AI feedback was more structured and referenced specific frameworks.
- Human feedback was practical and context-specific with experiential insights.
- Both feedback types covered similar themes like empathy and communication clarity.

## Abstract

Objectives

This pilot study aims to compare the quality of feedback provided by generative artificial intelligence (AI) and an official Royal College Examiner for simulated clinical and communication scenarios designed to prepare candidates for the Royal College of Ophthalmologists Part 2 Oral Examination.

Design

Utilising GPT 3.5 and 4 (OpenAI, San Francisco, CA, USA), an interactive web-based platform has been created that is able to simulate both patient and examiner roles in oral examination scenarios and simultaneously provide feedback on a candidate’s performance. Feedback was provided solely using GPT-4 in combination with prompt techniques. A standardised patient was used to enact five clinical and communication scenarios that were each assessed by both the AI and a Royal College of Ophthalmologists Examiner. The transcripts from these sessions were thematically analysed using NVivo software (Lumivero, Burlington, MA, USA) to compare the quality and content of the feedback from both sources.

Main outcome measures

To determine the similarities and differences in the content and structure of feedback provided by AI (Examiner A) and a Royal College Examiner (Examiner B) in the context of preparing candidates for the Fellowship of the Royal College of Ophthalmologists (FRCOphth) Part 2 Oral Examination.

Results

The results reveal that while both Examiner A and Examiner B provide feedback on similar themes, such as empathy, communication clarity and systematic clinical reasoning, their approaches differ. Examiner A’s feedback was more structured and often referenced specific frameworks, offering detailed, protocol-driven guidance. In contrast, Examiner B’s feedback was more practical and context-specific, focusing on real-world applications and providing nuanced insights shaped by experiential knowledge.

Conclusion

The findings suggest that generative AI has the potential to complement traditional oral exam preparation methods by providing easily accessible, structured and scalable feedback which could provide an early foundation of learning for candidates. It may be particularly useful for those unfamiliar with the specific requirements of Royal College examinations.